Several studies have shown that bone morphogenetic proteins (BMPs) can influence adipogenic and osteogenic cell lineages. We have shown that a peptide derived from BMP-9 (pBMP-9) at 400 ng/ml inhibits the proliferation of preosteoblasts and induces differentiation. We have now determined the effects of pBMP-9 (400 ng/ml) and equimolar concentrations of BMP-2 (100 ng/ml), BMP-9 (84.6 ng/ml) and pBMP-9 (9.04 ng/ml) on human white preadipocytes (HWP). pBMP-9 dose dependently reduced the proliferation of HWP without affecting the number of apoptotic cells. Incubation of the cells for 1 h with BMP-2, BMP-9 or pBMP-9 activated the Smad1/5/8 pathway, while incubation for 7 days in adipocyte differentiation (AD) serum-free medium containing ciglitazone and equimolar concentrations of BMP-2, BMP-9 or pBMP-9 enhanced the levels of mRNA of the adipogenic markers aP2 and adipoQ and increased the number of lipid vesicles. Thus, pBMP-9, like BMP-9, can increase the AD of HWP in AD serum-free medium.

Next basket recommendation is a critical task in market basket data analysis. It is particularly important in grocery shopping, where grocery lists are an essential part of shopping habits of many customers. In this work, we first present a new grocery Recommender System available on the MyGroceryTour platform. Our online system uses different traditional machine learning (ML) and deep learning (DL) algorithms, and provides recommendations to users in a real-time manner. It aims to help Canadian customers create their personalized intelligent weekly grocery lists based on their individual purchase histories, weekly specials offered in local stores, and product cost and availability information. We perform clustering analysis to partition given customer profiles into four non-overlapping clusters according to their grocery shopping habits. Then, we conduct computational experiments to compare several traditional ML algorithms and our new DL algorithm based on the use of a gated recurrent unit (GRU)-based recurrent neural network (RNN) architecture. Our DL algorithm can be viewed as an extension of DREAM (Dynamic REcurrent bAsket Model) adapted to multi-class (i.e. multi-store) classification, since a given user can purchase recommended products in different grocery stores in which these products are available. Among traditional ML algorithms, the highest average F-score of 0.516 for the considered data set of 831 customers was obtained using Random Forest, whereas our proposed DL algorithm yielded the average F-score of 0.559 for this data set. The main advantage of the presented Recommender System is that our intelligent recommendation is personalized, since a separate traditional ML or DL model is built for each customer considered. Such a personalized approach allows us to outperform the prediction results provided by general state-of-the-art DL models.

Estimating above-ground biomass in the context of fertilization management requires the monitoring of crops at early stages. Conventional remote sensing techniques make use of vegetation indices such as the normalized difference vegetation index (NDVI), but they do not exploit the high spatial resolution (ground sampling distance < 5 mm) now achievable with the introduction of unmanned aerial vehicles (UAVs) in agriculture. The aim of this study was to compare image mosaics to single images for the estimation of corn biomass and the influence of viewing angles in this estimation. Nadir imagery was captured by a high spatial resolution camera mounted on a UAV to generate orthomosaics of corn plots at different growth stages (from V2 to V7). Nadir and oblique images (30° and 45° with respect to the vertical) were also acquired from a zip line platform and processed as single images. Image segmentation was performed using the difference color index Excess Green-Excess Red, allowing for the discrimination between vegetation and background pixels. The apparent surface area of plants was then extracted and compared to biomass measured in situ. An asymptotic total least squares regression was performed and showed a strong relationship between the apparent surface area of plants and both dry and fresh biomass. Mosaics tended to underestimate the apparent surface area in comparison to single images because of radiometric degradation. It is therefore conceivable to process only single images instead of investing time and effort in acquiring and processing data for orthomosaic generation. When comparing oblique photography, an angle of 30° yielded the best results in estimating corn biomass, with a low residual standard error of orthogonal distance (RSEOD = 0.031 for fresh biomass, RSEOD = 0.034 for dry biomass). Since oblique imagery provides more flexibility in data acquisition with fewer constraints on logistics, this approach might be an efficient way to monitor crop biomass at early stages.

Ditylenchus dipsaci is a devastating pest to many crops worldwide. We present the first genome sequence for this species, produced with PacBio sequencing and assembled with CANU.

Understanding the evolution of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) and its relationship to other coronaviruses in the wild is crucial for preventing future virus outbreaks. While the origin of the SARS-CoV-2 pandemic remains uncertain, mounting evidence suggests the direct involvement of the bat and pangolin coronaviruses in the evolution of the SARS-CoV-2 genome. To unravel the early days of a probable zoonotic spillover event, we analyzed genomic data from various coronavirus strains from both human and wild hosts. Bayesian phylogenetic analysis was performed using multiple datasets, using strict and relaxed clock evolutionary models to estimate the occurrence times of key speciation, gene transfer, and recombination events affecting the evolution of SARS-CoV-2 and its closest relatives. We found strong evidence supporting the presence of temporal structure in datasets containing SARS-CoV-2 variants, enabling us to estimate the time of SARS-CoV-2 zoonotic spillover between August and early October 2019. In contrast, datasets without SARS-CoV-2 variants provided mixed results in terms of temporal structure. However, they allowed us to establish that the presence of a statistically robust clade in the phylogenies of gene S and its receptor-binding (RBD) domain, including two bat (BANAL) and two Guangdong pangolin coronaviruses (CoVs), is due to the horizontal gene transfer of this gene from the bat CoV to the pangolin CoV that occurred in the middle of 2018. Importantly, this clade is closely located to SARS-CoV-2 in both phylogenies. This phylogenetic proximity had been explained by an RBD gene transfer from the Guangdong pangolin CoV to a very recent ancestor of SARS-CoV-2 in some earlier works in the field before the BANAL coronaviruses were discovered. Overall, our study provides valuable insights into the timeline and evolutionary dynamics of the SARS-CoV-2 pandemic.

Purpose: Fungal diseases remain among the leading causes of global crop losses, with management still heavily reliant on fungicide applications. While traditional decision support systems and machine learning models offer valuable predictive insights, they often overlook the spatial and relational dynamics underlying pathogen spread. This study evaluates the feasibility and advantages of Graph Neural Networks (GNNs) for predicting fungal disease occurrence in three key crops—onion ( Botrytis squamosa ), lettuce ( Botrytis lactucae ), and carrot ( Cercospora carotae )—to enhance precision agriculture decision-making. Methods: Field observations from farms in southern Quebec were used to build plant-level graphs, with nodes representing plants enriched by biological and weather features, and edges defined by spatial proximity. Graph convolutional networks were trained for binary fungal disease occurrence classification and benchmarked against machine learning and deep learning baselines. Graph augmentation techniques and robustness tests under missing and noisy features were applied to assess GNN’s stability. Results: Across the three pathosystems, GNNs achieved the strongest overall predictive performance. For onions ( B. squamosa ), Random Forest slightly outperformed the GNN on the complete feature set (accuracy = 76.4% and F1-score = 0.77); here, the GNN provided lower but comparable metric scores (accuracy = 74.8% and F1-score = 0.73). For lettuce ( B. lactucae ), the GNN achieved the highest metric scores with the accuracy of 90.4% and F1-score of 0.90, surpassing all other baselines. For carrot ( C. carotae ), GNNs reached the accuracy of 75.8% and F1-score of 0.77, clearly outperforming Decision Tree, Random Forest, k-NN, and Feed-Forward Neural Networks (FFNs). Graph augmentation further improved the GNN results: random walk sampling increased the model’s accuracy on onion data to 79.3% and F1-score to 0.79, and on lettuce data to 93.9% and to 0.94, respectively, while node/edge perturbation improved the model’s accuracy on carrot data to 78.6% and F1-score to 0.80. Furthermore, the results of the robustness experiments suggest that GNNs can still track overall field-level infection trends with up to 75% of features masked or 50% replaced by noise. Conclusion: GNNs offer clear advantages for fungal disease occurrence prediction by incorporating spatial and relational plant patterns, thus improving both the accuracy and robustness of predicted outcomes.